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98_KLDivLoss • vectorized_kldiv_base_base

Level 1 • Task 98
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(predictions: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
    """
    Computes the Kullback-Leibler Divergence for comparing two distributions.

    Args:
        predictions (torch.Tensor): Predicted values.
        targets (torch.Tensor): Target values.

    Returns:
        torch.Tensor: Kullback-Leibler Divergence.
    """
    return F.kl_div(torch.log(predictions), targets, reduction="batchmean")


class Model(nn.Module):
    """
    A model that computes Kullback-Leibler Divergence for comparing two distributions.

    Parameters:
        None
    """

    def __init__(self):
        super(Model, self).__init__()

    def forward(self, predictions, targets, fn=module_fn):
        return fn(predictions, targets)


batch_size = 128
input_shape = (4096,)
dim = 1


def get_inputs():
    return [
        torch.randn(batch_size, *input_shape).softmax(dim=-1),
        torch.randn(batch_size, *input_shape).softmax(dim=-1),
    ]


def get_init_inputs():
    return []

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that computes Kullback-Leibler Divergence for comparing two distributions.

    Parameters:
        None
    """
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, predictions, targets):
        return torch.nn.functional.kl_div(torch.log(predictions), targets, reduction='batchmean')

batch_size = 128
input_shape = (4096, )
dim = 1

def get_inputs():
    return [torch.randn(batch_size, *input_shape).softmax(dim=-1), torch.randn(batch_size, *input_shape).softmax(dim=-1)]

def get_init_inputs():
    return []
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Kernel Information

Operation Name 98_KLDivLoss
Level ID 1
Task ID 98
Kernel Name vectorized_kldiv_base_base
CUDA Speedup (Native) 2.595x
CUDA Speedup (Compile) 2.931x
CUDA Runtime 0.012 ms
PyTorch Runtime (Native) 0.031 ms
PyTorch Runtime (Compile) 0.035 ms
Correct True
Max Diff (vs. Reference) 0.004000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 1, Task 98 • 98_KLDivLoss)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 optimized_kl_div_cuda_base 0.01 2.83 3.20
🥈 kl_div_sync_optimized_base 0.01 2.59 2.93
🥈 optimized_kl_div_kernel_base 0.01 2.59 2.93
🥈 kl_div_balanced_workload_base 0.01 2.59 2.93
🥈 kl_div_warp_reduce_base_base 0.01 2.59 2.93
🥈 optimized_kl_div_base 0.01 2.59 2.93
🥈 kl_div_modular_reduce_base_base 0.01 2.59 2.93
🥈 kldiv_optimized_stride_base_base_base 0.01 2.59 2.93
🥈 vectorized_aligned_kl_base 0.01 2.59 2.93
🥈 98_KLDivLoss_optimal_reduce_edit_1 0.01 2.59 2.93
🥈 strided_warp_kl_base_base 0.01 2.59 2.93
🥈 fast_strided_kl_base 0.01 2.59 2.93
🥈 coalesced_chunked_kl_base 0.01 2.59 2.93
🥈 kldiv_modular_per_thread_base_base 0.01 2.59 2.93
🥈 kldiv_unrolled_reduction_base_base 0.01 2.59 2.93
🥈 kl_div_unrolled_reduce_base_base 0.01 2.59 2.93
🥈 warp_block_vec4_opt_base 0.01 2.59 2.93
🥈 vectorized_kldiv_base_base 0.01 2.59 2.93
🥈 kl_div_even_workload_distribution_base 0.01 2.59 2.93
🥈 adaptive_kl_div_cuda_base 0.01 2.59 2.93 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

__device__ __forceinline__ float compute_kl_element(float log_pred, float target) {
    return expf(log_pred) - target * log_pred;
}

__device__ __forceinline__ float warp_reduce_sum(float val) {
    #pragma unroll
    for (int offset = 16; offset > 0; offset /= 2)
        val += __shfl_down_sync(0xffffffff, val, offset);
    return val;
}

__global__ void kl_div_kernel_vectorized(
    const float4* __restrict__ log_predictions,
    const float4* __restrict__ targets, 
    float* __restrict__ output,
    const int n_vec) {
    
    extern __shared__ float warp_sums[];
    
    const int warp_id = threadIdx.x / 32;
    const int lane = threadIdx.x % 32;
    const int warps_per_block = blockDim.x / 32;
    
    // Ensure coalesced memory access
    const int vec_idx = blockIdx.x * blockDim.x / 4 + threadIdx.x / 4;
    const int vec_stride = gridDim.x * blockDim.x / 4;
    
    float sum = 0.0f;
    
    if (vec_idx < n_vec) {
        float4 log_pred4 = log_predictions[vec_idx];
        float4 target4 = targets[vec_idx];
        
        sum += compute_kl_element(log_pred4.x, target4.x);
        sum += compute_kl_element(log_pred4.y, target4.y);
        sum += compute_kl_element(log_pred4.z, target4.z);
        sum += compute_kl_element(log_pred4.w, target4.w);
    }
    
    // Warp reduction
    sum = warp_reduce_sum(sum);
    
    if (lane == 0) {
        warp_sums[warp_id] = sum;
    }
    __syncthreads();
    
    // Final reduction by first warp
    if (warp_id == 0 && lane < warps_per_block) {
        float warp_sum = warp_sums[lane];
        warp_sum = warp_reduce_sum(warp_sum);
        
        if (lane == 0) {
            atomicAdd(output, warp_sum);
        }
    }
}

torch::Tensor kl_div_cuda_forward(
    torch::Tensor log_predictions,
    torch::Tensor targets) {
    
    const int n = log_predictions.numel();
    const int n_vec = (n + 3) / 4;
    auto output = torch::zeros({1}, log_predictions.options());
    
    const int threads = 128;
    const int blocks = min((n_vec * 4 + threads - 1) / threads, 1024);
    const int warps_per_block = threads / 32;
    const int shared_mem = warps_per_block * sizeof(float);
    
    kl_div_kernel_vectorized<<<blocks, threads, shared_mem>>>(
        reinterpret_cast<const float4*>(log_predictions.data_ptr<float>()),
        reinterpret_cast<const float4*>(targets.data_ptr<float>()),
        output.data_ptr<float>(),
        n_vec
    );
    
    return output / static_cast<float>(n);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &kl_div_cuda_forward, "KL divergence forward (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.712 inst/cycle 0.001 5
Executed Ipc Elapsed 0.250 inst/cycle 0.000 5
Issue Slots Busy 20.208 % 0.506 5
Issued Ipc Active 0.808 inst/cycle 0.001 5
SM Busy 20.208 % 0.506 5
Memory Throughput 212973031775.306 byte/second 1983425442819398144.000 5
Mem Busy 8.472 % 0.009 5
Max Bandwidth 6.418 % 0.002 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 48.620 % 0.016 5
Mem Pipes Busy 6.548 % 0.003 5
Warp Cycles Per Issued Instruction 32.204 cycle 2.403 5
Warp Cycles Per Executed Instruction 36.506 cycle 3.079 5
Avg. Active Threads Per Warp 29.830 0.000 5
Avg. Not Predicated Off Threads Per Warp 26.830 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 21.000 block 0.000 5
Block Limit Shared Mem 28.000 block 0.000 5
Block Limit Warps 16.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 40.628 % 0.111 5
Achieved Active Warps Per SM 26.000 warp 0.045 5
Analysis Rules
Rule Description
WRN HighPipeUtilization All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (40.5%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.
Operation / Metric Value Unit
aten::zeros
CPU Time 5032926.17 μs
Device Time 215992.82 μs
Self CPU Time 127321.34 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 5467651.13 μs
Device Time 7636034.92 μs
Self CPU Time 335880.60 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::fill_
CPU Time 5131771.96 μs
Device Time 7636034.92 μs
Self CPU Time 383961.70 μs
Self Device Time 7636034.92 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaLaunchKernel
CPU Time 5678641.08 μs
Device Time 2141.01 μs
Self CPU Time 5678641.08 μs
Self Device Time 2141.01 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
kl_div_kernel_vectorized(float4 const*, float4 const*, float*, int)
CPU Time 0.00 μs
Device Time 438944.78 μs
Self CPU Time 0.00 μs
Self Device Time 438944.78 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::div
CPU Time 984430.70 μs
Device Time 274710.43 μs
Self CPU Time 509853.71 μs
Self Device Time 274710.43 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void at::native::vectorized_elementwise_kernel<4, at::native::BUnaryFunctor<float, float, float, at::native::binary_internal::MulFunctor<float> >, at::detail::Array<char*, 2> >(int, at::native::BUnaryFunctor<float, float, float, at::native::binary_internal::MulFunctor<float> >, at::detail::Array<char*, 2>)
CPU Time 0.00 μs
Device Time 274733.18 μs
Self CPU Time 0.00 μs
Self Device Time 274733.18 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 7420042.10 μs
Self CPU Time 0.00 μs
Self Device Time 7420042.10 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Failed 
45249 warnings and 1 error generated when compiling for host.
Error while processing /home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu.
Suppressed 45287 warnings (45240 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
Found compiler error(s).
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:17:5 bugprone-easily-swappable-parameters
17 | const float4* __restrict__ log_predictions,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
18 | const float4* __restrict__ targets,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:17:32: note: the first parameter in the range is 'log_predictions'
17 | const float4* __restrict__ log_predictions,
| ^~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:18:32: note: the last parameter in the range is 'targets'
18 | const float4* __restrict__ targets,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:24:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
24 | const int warp_id = threadIdx.x / 32;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:25:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | const int lane = threadIdx.x % 32;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:26:33: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | const int warps_per_block = blockDim.x / 32;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:29:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
29 | const int vec_idx = blockIdx.x * blockDim.x / 4 + threadIdx.x / 4;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:30:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | const int vec_stride = gridDim.x * blockDim.x / 4;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:64:19: warning: the parameter 'log_predictions' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
64 | torch::Tensor log_predictions,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:65:19: warning: the parameter 'targets' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
65 | torch::Tensor targets) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:67:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
67 | const int n = log_predictions.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250213_optimize_b10_s4_e0_models_2/level_1/task_98/b10_s2_vectorized_kldiv_base/base/base.cu:72:24: error: no matching function for call to 'min' [clang-diagnostic-error]
72 | const int blocks = min((n_vec * 4 + threads - 1) / threads, 1024);
| ^~~
/home/common_modules/clang-tidy/20.0.0git/lib/clang/20/include/__clang_cuda_math.h:201:16: note: candidate function not viable: call to __device__ function from __host__ function
201 | __DEVICE__ int min(int __a, int __b) { return __nv_min(__a, __b); }
| ^
/usr/local/cuda/include/crt/math_functions.hpp:868:38: note: candidate function not viable: call to __device__ function from __host__ function
868 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:873:38: note: candidate function not viable: call to __device__ function from __host__ function
873 | __MATH_FUNCTIONS_DECL__ unsigned int min(const int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:878:38: note: candidate function not viable: call to __device__ function from __host__ function
878 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:883:34: note: candidate function not viable: call to __device__ function from __host__ function
883 | __MATH_FUNCTIONS_DECL__ long int min(const long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:902:43: note: candidate function not viable: call to __device__ function from __host__ function
902 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:919:43: note: candidate function not viable: call to __device__ function from __host__ function
919 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:936:43: note: candidate function not viable: call to __device__ function from __host__ function
936 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:953:39: note: candidate function not viable: call to __device__ function from __host__ function
953 | __MATH_FUNCTIONS_DECL__ long long int min(const long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:958:48: note: candidate function not viable: call to __device__ function from __host__ function
958 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:963:48: note: candidate function not viable: call to __device__ function from __host__ function
963 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:968:48: note: candidate function not viable: call to __device__ function from __host__ function
968 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:973:31: note: candidate function not viable: call to __device__ function from __host__ function
973 | __MATH_FUNCTIONS_DECL__ float min(const float a, const float b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:978:32: note: candidate function not viable: call to __device__ function from __host__ function
978 | __MATH_FUNCTIONS_DECL__ double min(const double a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:983:32: note: candidate function not viable: call to __device__ function from __host__ function
983 | __MATH_FUNCTIONS_DECL__ double min(const float a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:988:32: note: candidate function not viable: call to __device__ function from __host__ function
988 | __MATH_FUNCTIONS_DECL__ double min(const double a, const float b)
| ^